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Hot charge carriers (HC) are photoexcited electrons and holes that exist in nonequilibrium high-energy states of photoactive materials. Prolonged cooling time and rapid extraction are the current challenges for the development of future innovative HC-based optoelectronic devices, such as HC solar cells (HCSCs), hot energy transistors (HETs), HC photocatalytic reactors, and lasing devices. Based on a thorough analysis of the basic mechanisms of HC generation, thermalization, and cooling dynamics, this review outlines the various possible strategies to delay the HC cooling as well as to speed up their extraction. Various materials with slow cooling behavior, including perovskites and other semiconductors, are thoroughly presented. In addition, the opportunities for the generation of plasmon-induced HC through surface plasmon resonance and their technological applications in hybrid nanostructures are discussed in detail. By judiciously designing the plasmonic nanostructures, the light coupling into the photoactive layer and its optical absorption can be greatly enhanced as well as the successful conversion of incident photons to HC with tunable energies can also be realized. Finally, the future outlook of HC in optoelectronics is highlighted which will provide great insight to the research community.In photoactive materials, the fundamental understandings of hot charge carriers and a successful device design are the current challenges for the development of highly efficient hot carrier optoelectronic devices.

In radiotherapy for cancer patients, an indispensable process is to delineate organs-at-risk (OARs) and tumors. However, it is the most time-consuming step as manual delineation is always required from radiation oncologists. Herein, we propose a lightweight deep learning framework for radiotherapy treatment planning (RTP), named RTP-Net, to promote an automatic, rapid, and precise initialization of whole-body OARs and tumors. Briefly, the framework implements a cascade coarse-to-fine segmentation, with adaptive module for both small and large organs, and attention mechanisms for organs and boundaries. Our experiments show three merits: 1) Extensively evaluates on 67 delineation tasks on a large-scale dataset of 28,581 cases; 2) Demonstrates comparable or superior accuracy with an average Dice of 0.95; 3) Achieves near real-time delineation in most tasks with <2 s. This framework could be utilized to accelerate the contouring process in the All-in-One radiotherapy scheme, and thus greatly shorten the turnaround time of patients. Volume delineation of organs-at risk (OARs) and target tumors is an indispensable process for creating radiotherapy treatment planning. Herein, the authors propose a lightweight deep learning framework to empower the rapid and precise volume delineation of whole-body OARs and target tumors.

Observational studies using causal inference frameworks can provide a feasible alternative to randomized controlled trials. Advances in statistics, machine learning, and access to big data facilitate unraveling complex causal relationships from observational data across healthcare, social sciences, and other fields. However, challenges like evaluating models and bias amplification remain.

Background The relationship between lung function and sarcopenia remains ambiguous. The primary aim of this study was to investigate the potential association between lung function and sarcopenia in the older adults, as well as to examine the mediating role of cognitive function in this relationship. Methods The participants were selected from a nationally representative population-based cohort in China. The peak expiratory flow (PEF) measurement was used to evaluate the lung function in older persons. The sarcopenia was diagnosed using the guidelines of the Asian Working Group for Sarcopenia (AWGS) in 2019. The Cox proportional hazard model was utilized to perform primary analyses of the relationship between PEF and sarcopenia. The mediating effect of cognitive function was evaluated using the counterfactual mediation method. Results This cohort study included 4,011 older adults (average age, 66.6 years; 53.3% males). During a follow-up period of 3.86 years, 349 individuals were diagnosed with sarcopenia. After adjusting for potential confounders, each one-standard-deviation increase in PEF was associated with a 28% reduction in the risk of sarcopenia (hazard ratio [HR]: 0.72; 95% confidence interval [CI]: 0.63, 0.80). There was a significant mediation of cognition for the association between PEF and incident sarcopenia, and the proportion mediated was 12.2% (95% CI: 4.5%, 23.1%). Conclusions Older adults with impaired lung function are more likely to develop sarcopenia. Nevertheless, cognition can explain only a small portion of this association. Thus, other potential pathways between lung function and sarcopenia must be elucidated.

Tin-based perovskite solar cells have garnered attention for their biocompatibility, narrow bandgap, and long thermal carrier lifetime. However, nip -type tin-based perovskite solar cells have underperformed largely due to the indiscriminate use of metal oxide electron transport layers originally designed for nip -type lead-based perovskite solar cells. Here, we reveal that this underperformance is caused by oxygen vacancies and deeper energy levels in metal oxide. To address these issues, we propose a metal chalcogenide electron transport layer, specifically Sn(S 0.92 Se 0.08 ) 2 , which circumvents the oxygen molecules desorption and impedes the Sn 2+ oxidation. As a result, tin-based perovskite solar cells with Sn(S 0.92 Se 0.08 ) 2 demonstrate a V OC increase from 0.48 – 0.73 V and a power conversion efficiency boost from 6.98 – 11.78%. Additionally, these cells exhibit improved stability, retaining over 95% of their initial efficiency after 1632 h. Our findings showcase metal chalcogenides as promising candidates for future nip -type tin-based perovskite solar cell applications. nip-Type tin-based perovskite solar cells have underperformed largely due to the metal oxide electron transport layers originally designed for lead-based devices. Here, authors employ metal chalcogenide as the electron transport layer, achieving enhanced efficiency up to 11.78% for stable devices.

The photovoltaic performance of perovskite solar cell is determined by multiple interrelated factors, such as perovskite compositions, electronic properties of each transport layer and fabrication parameters, which makes it rather challenging for optimization of device performances and discovery of underlying mechanisms. Here, we propose and realize a novel machine learning approach based on forward-reverse framework to establish the relationship between key parameters and photovoltaic performance in high-profile MASnxPb1-xI3 perovskite materials. The proposed method establishes the asymmetrically bowing relationship between band gap and Sn composition, which is precisely verified by our experiments. Based on the analysis of structural evolution and SHAP library, the rapid-change region and low-bandgap plateau region for small and large Sn composition are explained, respectively. By establishing the models for photovoltaic parameters of working photovoltaic devices, the deviation of short-circuit current and open-circuit voltage with band gap in defective-zone and low-bandgap-plateau regions from Shockley-Queisser theory is captured by our models, and the former is due to the deep-level traps formed by crystallographic distortion and the latter is due to the enhanced susceptibility by increased Sn4+ content. The more difficulty for hole extraction than electron is also concluded in the models and the prediction curve of power conversion efficiency is in a good agreement with Shockley-Queisser limit. With the help of search and optimization algorithms, an optimized Sn:Pb composition ratio near 0.6 is finally obtained for high-performance perovskite solar cells, then verified by our experiments. Our constructive method could also be applicable to other material optimization and efficient device development.The forward-reverse framework based on machine learning for MASnxPb1-xI3 perovskite solar cells is reported. The practicability of bandgap model revealing asymmetrically-bowing shape and optimized Sn:Pb ratio are verified by experiments.

Telomere length (TL) has been recognized to be fundamental to the risk of autoimmune disorders. However, the role of leukocyte TL in Graves' disease has not yet been fully elucidated. In the study, we exploited the two-sample Mendelian randomization (MR) design to evaluate the causal effect of leukocyte TL on the risk of Graves' disease. Genome-wide association study (GWAS) data of leukocyte TL from the Singapore Chinese Health Study (SCHS) cohort and Graves' disease from Biobank Japan (BBJ, 2176 cases and 210,277 controls) were analyzed. Nine single nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs) for TL. We used the inverse variance weighted (IVW) approach as the main estimator and MR-Egger regression, weighted median, simple mode, and weighed mode methods as complementary estimators. Horizontal pleiotropy was assessed using the intercept from MR-Egger. The analysis demonstrated that genetically predicted longer leukocyte TL was causally associated with a lower risk of Graves' disease using the IVW method (odds ratio [OR]: 1.64, 95% confidence interval [CI]: 1.23-2.17, =2.27e-04, and other complementary MR approaches achieved similar results. The intercept from the MR-Egger analysis provided no noticeable evidence of horizontal pleiotropy ( =0.02, =0.641). MR-PRESSO method reported no outliers ( =0.266). Our results provided evidence to support a genetic predisposition to shorter leukocyte TL with an increased risk of Graves' disease. Further studies are warranted to explore the mechanism underlying the association.

Background Some studies have shown racial and ethnic disparities in sleep debt, trouble sleeping, and daytime sleepiness, as well as the association between social demographics and sleep behaviors, such as educational level, ethnic origin, and marital status. However, most existing studies do not quantify the extent to which group-level disparities are explained by differences in the distribution of these factors. The present study employs Blinder-Oaxaca decomposition to examine the contributions of individual-level factors to disparities in average healthy sleep scores across diverse race and ethnicity groups, to inform strategies to mitigate healthy sleep pattern inequities. Methods In the National Health and Nutrition Examination Surveys (NHANES) from 2005–2008 and 2015–2018, Blinder–Oaxaca decomposition was used to assess the extent to which differences in individual-level factors (age, sex, marital status, education level, smoking status, alcohol intake, physical activity, BMI, place of birth, food security, health insurance, and household income) explain racial and ethnic disparities in average healthy sleep scores among Hispanic, non-Hispanic Black adults, or other races compared with non-Hispanic White adults. The healthy sleep score was constructed based on five components: 7–8 h of sleep per day, no trouble sleeping, no snoring, no excessive daytime sleepiness, and no nocturia. Each component was assigned 1 point for a favorable status. Results Among 17,476 participants (who represent an estimated 109,108,231 non-institutionalized US adults aged ≥ 20 years after applying sample weights), 67.8% were non-Hispanic White adults, 13.7% were Hispanic adults, 10.9% were non-Hispanic Black adults, and 7.6% belonged to other racial/ethnic groups. Among US adults, average (SE) healthy sleep score was 3.12 (0.03) in Hispanic, 2.84 (0.02) in non-Hispanic Black, 3.03 (0.02) in non-Hispanic White, and 3.15 (0.04) in other race/ethnicity adults. Blinder-Oaxaca decomposition analysis showed that education level, age, food security, and BMI were important components in explaining disparities in healthy sleep scores among racial and ethnic groups. Specifically, compared with non-Hispanic White adults, higher educational level and lower prevalence of food insecurity among non-Hispanic Black adults were associated with higher healthy sleep scores (differences attributable to education: +0.03 [SE = 0.01]; to food security: +0.06 [SE = 0.01], both P  < 0.001). Similarly, among Hispanic adults, younger average age and lower educational level were associated with higher healthy sleep scores (differences attributable to age: +0.08 [SE = 0.01]; to education: +0.11 [SE = 0.03], both P  < 0.001). Conclusions Factors such as education level, age, food security, and BMI substantially contribute to the racial and ethnic disparities in average healthy sleep scores among US adults.

With the rapid expansion of social media platforms, the demand for health information has increased substantially, leading to innovative approaches and new opportunities in health education. This study aims to analyze the characteristics of articles published on the \"Dr Ding Xiang\" WeChat official account (WOA), one of the most popular institutional accounts on the WeChat platform, to identify factors influencing readership engagement and to propose strategies for enhancing the effectiveness of health information dissemination. A total of 5286 articles published on the \"Dr Ding Xiang\" WOA from January 2021 to December 2021 were collected and analyzed. Additionally, a random sample of 324 articles was selected for detailed text analysis. Univariate analysis was conducted using the chi-square test, and multivariate analysis was performed using multivariable logistic regression. In 2021, the total number of reads for \"Dr Ding Xiang\" articles reached 323,479,841, with an average of 61,196 reads per article. Articles exceeding 100,000 reads accounted for 33.90% of the total. Most articles were published during the time slots of 8:00-10:00 AM, 11:30 AM to 1:30 PM, and 8:30-10:30 PM. Analysis indicated that the order of publication, style of the title sentence, number of likes, number of in-views, total likes on comments, and number of replies to comments were significantly associated with an article's number of reads. Text analysis further revealed that the article's reasoning approaches and concluding methods also had a significant impact on readership. To enhance readership and the effectiveness of health communication, health-focused WOAs should consider key factors such as optimal publication timing, engaging title design, and effective content structuring. Attention to these elements can improve user engagement and support the broader dissemination of health information.

Realization of remote wearable health monitoring (RWHM) technology for the flexible photodiodes is highly desirable in remote‐sensing healthcare systems used in space stations, oceans, and forecasting warning, which demands high external quantum efficiency (EQE) and detectivity in NIR region. Traditional inorganic photodetectors (PDs) are mechanically rigid and expensive while the widely reported solution‐processed mixed tin‐lead (MSP) perovskite photodetectors (PPDs) exhibit a trade‐off between EQE and detectivity in the NIR region. Herein, a novel functional passivating antioxidant (FPA) strategy has been introduced for the first time to simultaneously improve crystallization, restrain Sn2+ oxidization, and reduce defects in MSP perovskite films by multiple interactions between thiophene‐2‐carbohydrazide (TAH) molecules and cations/anions in MSP perovskite. The resultant solution‐processed rigid mixed Sn–Pb PPD simultaneously achieves high EQE (75.4% at 840 nm), detectivity (1.8 × 1012 Jones at 840 nm), ultrafast response time (trise/tfall = 94 ns/97 ns), and improved stability. This work also highlights the demonstration of the first flexible photodiode using MSP perovskite and FPA strategy with remarkably high EQE (75% at 840 nm), and operational stability. Most importantly, the RWHM is implemented for the first time in the PIN MSP perovskite photodiodes to remotely monitor the heart rate of humans at rest and after‐run conditions. A novel functional passivating antioxidant strategy is introduced to simultaneously improve crystallization, restrain Sn2+ oxidization, and reduce defects in mixed tin‐lead (MSP) perovskite films. This work highlights the first flexible photodiode using MSP perovskite with remarkable performance. Finally, the remote wearable health monitoring (RWHM) is implemented for the first time in the PIN MSP perovskite photodiodes to remotely monitor the heart rate of humans.